Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST
The Fourth Industrial Revolution, also known as Industry 4.0, which is the intensified digitalization and automation in industry, embraces cyber–physical systems, the Internet of Things (IoT), and artificial intelligence, among others. This study utilizes Fuzzy Integration–Rough Set Theory (FI-RST)...
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MDPI AG
2024-10-01
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| author | Qingwen Li Waifan Tang Zhaobin Li |
| author_facet | Qingwen Li Waifan Tang Zhaobin Li |
| author_sort | Qingwen Li |
| collection | DOAJ |
| description | The Fourth Industrial Revolution, also known as Industry 4.0, which is the intensified digitalization and automation in industry, embraces cyber–physical systems, the Internet of Things (IoT), and artificial intelligence, among others. This study utilizes Fuzzy Integration–Rough Set Theory (FI-RST) analysis to quantify the impacts of the imperative Industry 4.0 technologies for manufacturing firms located in Fujian Province, China, namely, Manufacturing Execution Systems (MES), the Industrial Internet of Things (IIoT), and Additive Manufacturing (AM), on the sustainable development performance of firms. The findings of the study indicate that these technologies greatly improve the effectiveness of the utilization of resources, reduce the costs of operations, and reduce the impact on the environment. In addition, they have a favorable influence on social considerations, such as preserving the well-being of employees and the outcome of training programs. This research work has convincingly provided an underlying strategic adoption of these technologies for sustainability production by raising important insights that could be valuable for industry managers and policymakers, especially those seeking sustainability at the global level. |
| format | Article |
| id | doaj-art-757584ac1bf6429cb1228fd386320897 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-757584ac1bf6429cb1228fd3863208972025-08-20T02:10:57ZengMDPI AGApplied Sciences2076-34172024-10-011420954510.3390/app14209545Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RSTQingwen Li0Waifan Tang1Zhaobin Li2Department of Construction and Quality Management, School of Science and Technology, Hong Kong Metropolitan University, Homantin Kowloon, Hong Kong SAR 999077, ChinaDepartment of Construction and Quality Management, School of Science and Technology, Hong Kong Metropolitan University, Homantin Kowloon, Hong Kong SAR 999077, ChinaDepartment of Construction and Quality Management, School of Science and Technology, Hong Kong Metropolitan University, Homantin Kowloon, Hong Kong SAR 999077, ChinaThe Fourth Industrial Revolution, also known as Industry 4.0, which is the intensified digitalization and automation in industry, embraces cyber–physical systems, the Internet of Things (IoT), and artificial intelligence, among others. This study utilizes Fuzzy Integration–Rough Set Theory (FI-RST) analysis to quantify the impacts of the imperative Industry 4.0 technologies for manufacturing firms located in Fujian Province, China, namely, Manufacturing Execution Systems (MES), the Industrial Internet of Things (IIoT), and Additive Manufacturing (AM), on the sustainable development performance of firms. The findings of the study indicate that these technologies greatly improve the effectiveness of the utilization of resources, reduce the costs of operations, and reduce the impact on the environment. In addition, they have a favorable influence on social considerations, such as preserving the well-being of employees and the outcome of training programs. This research work has convincingly provided an underlying strategic adoption of these technologies for sustainability production by raising important insights that could be valuable for industry managers and policymakers, especially those seeking sustainability at the global level.https://www.mdpi.com/2076-3417/14/20/9545industry 4.0sustainable manufacturingfuzzy integration-rough set theorymanufacturing execution systemsindustrial internet of things |
| spellingShingle | Qingwen Li Waifan Tang Zhaobin Li Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST Applied Sciences industry 4.0 sustainable manufacturing fuzzy integration-rough set theory manufacturing execution systems industrial internet of things |
| title | Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST |
| title_full | Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST |
| title_fullStr | Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST |
| title_full_unstemmed | Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST |
| title_short | Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST |
| title_sort | leveraging industry 4 0 for sustainable manufacturing a quantitative analysis using fi rst |
| topic | industry 4.0 sustainable manufacturing fuzzy integration-rough set theory manufacturing execution systems industrial internet of things |
| url | https://www.mdpi.com/2076-3417/14/20/9545 |
| work_keys_str_mv | AT qingwenli leveragingindustry40forsustainablemanufacturingaquantitativeanalysisusingfirst AT waifantang leveragingindustry40forsustainablemanufacturingaquantitativeanalysisusingfirst AT zhaobinli leveragingindustry40forsustainablemanufacturingaquantitativeanalysisusingfirst |